Our comparison methods include the traditional dehazing algorithms, e.g., DCP [1]}, CAP [2]}, Meng et al. [3]} and AMEF [4]}, as well as the CNN-based methods, e.g., DehazeNet [5]}, AOD-Net [6]}, GFN [7]}, DCPDN [8]}, GCANet [9]}, DuRN [10]}, GridNet [11]}, FD-GAN [12]} and MSBDN [13]}. These objective quality metrics, PSNR, SIMM (i.e., the higher, the better) and perceptual quality LPIPS [14]} (i.e., the lower, the better), are used to measure the dehazing performance. TABLE I reports the quantitative results on synthetic datasets SOTS indoor, SOTS outdoor and TestA-DCPDN as well as real-world dataset NH-HAZE, from which we can clearly observe that our proposed method performs best in terms of PSNR, SSIM and LPIPS metrics. Compared with the latest methods for each dataset for PSNR, SSIM and LPIPS value, our previous method [15]} are better than those of the second place method with significant margins on SOTS indoor, SOTS outdoor, TestA-DCPDN and NH-HAZE, respectively. Furthermore, our improved method NHRN performs better than our previous method [15]} in terms of PSNR, SSIM and LPIPS metrics on four datasets, especially for the NH-HAZE dataset.
